Journal of Quality Engineering and Management

Abstract Purpose: With the rapid growth of the financial technology (FinTech) industry, digital advertising has become one of the key tools for attracting new customers and increasing the loyalty of existing ones. In environments where uncertainty and decision-making complexity play significant roles, the use of metaheuristic algorithms can help optimize digital advertising efforts. Methodology: This study proposes a three-level model in an intuitionistic fuzzy environment and utilizes the Stackelberg game to examine the impact of advertising on performance, customer acquisition, and customer loyalty. In this study, the advertising process of FinTech companies is modeled as a three-level decision-making process encompassing customer acquisition, advertising performance, and customer loyalty. To solve this model, Genetic Algorithms (GA) and Particle Swarm Optimization (PSO) are employed to optimize advertising strategies. Findings: The results indicated that the proposed model accurately predicted customer loyalty and that metaheuristic algorithms effectively optimized advertising parameters. The analysis of the results showed that conversion rate and purchase amount are the most influential factors affecting customer loyalty. Furthermore, the findings revealed that using hybrid algorithms can reduce advertising costs and increase Return on Investment (ROI). Comparing the proposed algorithms showed that the hybrid approach, combining genetic algorithms and particle swarm optimization, outperformed the individual methods in predicting customer behavior. Originality/Value: Based on the findings, it is recommended that FinTech companies adopt metaheuristic algorithms to optimize digital advertising and achieve precise customer targeting. These approaches can enhance advertising effectiveness, reduce marketing costs, and improve customer loyalty within the FinTech industry.

Abstract Purpose: This paper aims to accurately estimate the parameters of the fractional Ornstein-Uhlenbeck model using the Bayesian method and the SIR simulation algorithm and to compare its performance with the Maximum Likelihood Estimation (MLE) method in the context of stochastic differential models with long-memory properties. The paper also seeks to evaluate the efficiency of the Bayesian approach in similar models, particularly in analyzing financial data with long-term dependencies. Methodology: In this study, the parameters of the fractional Ornstein-Uhlenbeck model are estimated for the first time using the Bayesian method, with appropriate prior distributions and the SIR algorithm employed for simulation. The efficiency of the Bayesian estimator is compared with that of the MLE estimator using RMSE and variance indices. Findings: The results demonstrate that the Bayesian estimator provides more accurate parameter estimates than the Maximum Likelihood method. Moreover, as the degree of long-term data dependence increases, the accuracy of estimates improves under both methods; however, the Bayesian approach consistently outperforms the MLE. Additionally, the parameter σ is estimated with higher precision compared to the parameters k and μ. Originality/Value: The originality of this paper lies in the application of the SIR algorithm to estimate the parameters of the fractional Ornstein-Uhlenbeck model. This approach has not been previously explored. This innovation represents a significant contribution to the application of Bayesian methods for parameter estimation in stochastic differential models with long-memory properties, and it opens new avenues for applying similar techniques to models such as the Heston model in future research.

Abstract Purpose: This study aims to evaluate and rank collaboration models in the Iranian rubber industry supply chain from the perspective of digitalization quality. Digitalization quality refers to the effective use of Industry 4.0 technologies to improve transparency, integration, agility, resilience, and sustainability. The rubber industry was selected due to its operational complexity and urgent need for digital transformation. Methodology: A multi-criteria decision-making approach was adopted, combining the Fuzzy Best-Worst Method (BWM) for weighting the evaluation criteria and TOPSIS for ranking the collaboration models. A sensitivity analysis was also conducted to assess the robustness of the results across varying criterion weights. Findings: The digital supply chain model ranks highest in digitalization quality, with "technology integration" as the most critical criterion. The sensitivity analysis confirms the rankings' robustness and stability across different weight scenarios. Originality/Value: This research uniquely addresses the comparative assessment of collaboration models in the rubber industry based on digitalization quality. The use of a Fuzzy BWM-TOPSIS hybrid method and comprehensive sensitivity analysis provides a novel, practical framework for strategic decision-making in digital supply chain transformation.

Abstract Purpose: The most important effects of the Internet of Things in healthcare include the ability to exchange information, reduce hospitalization costs, and improve healthcare costs. The primary challenges of the Internet of Things in healthcare are security and privacy, with image transmission particularly crucial for communication and security. The primary objective of this paper is to design a suitable channel for transmitting medical data via chaotic synchronization that employs fuzzy modeling.
Methodology: This paper presents a new method for transmitting medical images to preserve patient information by synchronizing two fractional-order convolutional neural networks based on polynomial fuzzy modeling. Using chaotic signals as a carrier for medical images and employing a suitable fuzzy controller for synchronization at the receiver enhances security and significantly reduces the likelihood of detection. In this scheme, a suitable fuzzy controller is designed to establish the stability of the closed-loop system. Then, considering the synchronization scheme based on the polynomial fuzzy model and its error detection, a chaotic masking method is proposed to encrypt patient-related images.
Findings: Simulations have been performed on color and black-and-white medical images. Encrypted and recovered images have been obtained using this scheme. The simulation and accuracy of the proposed method's results have been investigated using MATLAB software. To evaluate the performance of the proposed method, various criteria, including image histogram, signal-to-noise ratio, correlation, and information entropy, were assessed. The results demonstrate the effectiveness of the proposed method in image encryption.
Originality/Value: This paper presents a new method for transmitting medical images to preserve patient information by synchronizing two fractional-order multi-convolutional systems based on polynomial fuzzy modeling. Using chaotic signals as a carrier for medical images and employing a suitable fuzzy controller for synchronization at the receiver enhances security and significantly reduces the likelihood of detection. In this project, a suitable fuzzy controller is designed to establish the stability of the closed-loop system. Then, considering the multi-state synchronization scheme based on the polynomial fuzzy model and its error detection, a chaotic masking method is proposed to encrypt patient-related images.

Abstract Purpose: Traditional banking needs new FinTech innovations and technologies to improve its processes and services. FinTech innovations have led to significant changes in the banking system, including advancements in risk management. Therefore, the present study aimed to investigate and analyze the heterogeneity and the mechanism underlying the effect of FinTech innovation on the risk-taking of commercial banks using balanced panel data from 20 banks for the period 2013-2022. Methodology: Based on web technology, an indicator at the bank level is considered, including the creation, annual number, and frequency of news related to fintech innovation from each bank. This indicator is calculated as the ratio of the value of online shopping and bill payments made through the Internet and mobile devices to GDP. To address potential endogeneity issues, including measurement errors and omitted variables, the methods of Instrumental Variables (IV) and Difference-in-Differences (DID) were employed to test the hypothesis and obtain consistent estimates. Findings: Showed that improvement in FinTech bank innovation significantly reduces risk-taking. The results of the mechanism analysis indicate that a bank's FinTech innovation reduces its risk-taking through two channels: increasing operating income and enhancing the capital adequacy ratio. The analysis of the heterogeneity of bank size, bank type, and competitiveness shows that larger, public, private, and highly competitive commercial banks have a more pronounced effect on reducing risk-taking in the development of technological innovation. Also, robustness and stability tests, including changing the methods used to construct the FinTech innovation index, replacing risk-taking indicators, and reducing the change in the study sample, showed that the findings remained unchanged. Originality/Value: The banking system should adopt a development model aligned with the era and utilize FinTech solutions to accelerate its digital transformation. Finally, since the use of FinTech by commercial banks presents certain potential risks, banks should enhance their risk management. Implement applicable supervisory measures, such as information disclosure standards and risk management indicators.

Abstract Purpose: This study aims to propose a fair fixed cost allocation scheme among a set of Decision-Making Units (DMUs), such as banks or factories, in an uncertain environment. The allocation is designed so as not to reduce DMU efficiency and may even lead to efficiency improvements. Methodology: To achieve this goal, a model is developed based on Data Envelopment Analysis (DEA) integrated with robust optimization. The inputs and outputs of the DMUs are treated as fuzzy random variables to reflect environmental uncertainty. The model is linearized and converted into a deterministic programming model using principles from stochastic programming. Furthermore, a common set of weights is used to ensure fairness in the allocation process. Findings: The results indicate that, under the proposed fixed-cost allocation plan, the DMUs' (banks') efficiency scores are not only maintained but, in many cases, improved, confirming the model's effectiveness in preserving and enhancing performance under uncertain conditions. Originality/Value: The novelty of this research lies in integrating DEA and robust optimization in uncertain environments to design a cost allocation model that ensures non-decreasing efficiency. Using a common set of weights enhances the approach's fairness. Additionally, applying the model to the Iranian banking sector highlights its practical relevance and managerial value.